Author(s): TANVEER MUSTAFA

Originally published on Towards AI.

From Model Training to Production Monitoring — A Complete Walkthrough

Building a machine learning model is one thing — deploying it to production and keeping it running reliably is another challenge entirely. This hands-on guide walks you through creating a complete ML pipeline on AWS SageMaker, with special focus on model serving, latency optimization, performance tracking, data drift detection, and handling model degradation.

This article provides a comprehensive tutorial on building an end-to-end machine learning pipeline using AWS SageMaker, covering environment setup, data preparation, model training, endpoint deployment, and ongoing performance monitoring and optimization. Key stages include configuring for auto-scaling during high traffic, implementing data drift detection, and setting up model performance tracking with CloudWatch. It emphasizes best practices for managing production environments, troubleshooting common issues, and ensuring model reliability through continuous monitoring and the ability to retrain as needed.

Read the full blog for free on Medium.

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Published via Towards AI